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Why do (some) ecologists have evolution envy?

I wrote a book on community ecology while on sabbatical last year. In a paper published a few years ago I communicated the core theme: the huge number of models, ideas, and theories in community ecology can mostly* be boiled down to different combinations of just four “high-level” processes – selection, drift, dispersal and speciation. This core idea leads to what I think is a different and more conceptually coherent view of theoretical community ecology, and the book was my attempt (an experiment of sorts) to see if it “works”. Of course I think it does, but you all can be the judge eventually.

While writing the book, I read hundreds of papers and book chapters and books**, spent countless hours pondering what they all collectively have to say, and so generated far more in the way of thoughts and ideas than could reasonably fit into a shortish book. Chatting about these with Jeremy last year prompted the idea of some guest posts on DE, which I decided was a good idea, both for the normal public-benefit reasons of provoking thought and discussion in the scientific community, and also for the selfish reason of perhaps piquing enough interest among DE readers to increase book sales (available in late summer 2016 from Princeton University Press!). (I can assure you there’s no financial motivation here: I will make no more than a few dollars off of each book sold, which I promise to pay you back with a beer at a conference if you bring the book to use as a coaster.)

So, here’s one line of questioning I find quite interesting. Why do some ecologists (such as myself) envy the theoretical state of evolutionary biology? Is this affliction justified?

First off, why evolution envy? On Team Evolution we have Darwin (natural selection) and Mendel (heredity), and then the Modern Synthesis that ties everything together into a lovely coherent whole in which the models all boil down to different combinations and manifestations of four main processes: selection, drift, gene flow, and mutation (sound familiar?). Who wouldn’t want to be on that team? Notwithstanding modern developments that have led some to question the adequacy of the Modern Synthesis, it’s a beautiful thing and a huge attraction of the field of evolutionary biology. As a result, evolutionary biologists all speak the same theoretical language, so there’s a feeling of oneness in the discipline that must make them all feel warm and fuzzy all the time (and perhaps a smidgen smug)***.

Ecology, in contrast, has splintered into innumerable sub-disciplines (e.g., population vs. ecosystem ecology) and sub-sub-disciplines (e.g., food-webs and coexistence of competitors within community ecology), each of which has its own distinct dialect, such that practitioners can hardly communicate with one another, and everyone spends excessive amounts of time arguing why their sub-sub-discipline is more important than the next. Who would want to be on that team? In community ecology (the focus of my book), one consequence of this is that students have great difficulty figuring out the connections between island biogeography, resource-competition theory, the intermediate-disturbance hypothesis, and multivariate ordination (just to name a few topics one might cover under this umbrella). The number of models and hypotheses keeps growing every larger, with reductions (i.e., global rejection and discarding of hypotheses) exceedingly rare. So, it’s a conceptual basket case.

Focusing on the “micro” portion of evolution and noting that population genetics theory is the conceptual core, then a great quote from Van Valen and Pitelka sums it up nicely: “Unlike population genetics, ecology has no known underlying regularities in its basic processes”.

Next question: Is evolution envy justified? Another way to approach this question is to ask: is evolutionary biology actually “better” at anything than ecology (as opposed to just being more aesthetically pleasing)? If so, then surely we are justified in having evolution envy. This question potentially opens the huge can of worms regarding what constitutes success in a given branch of science (no doubt Jeremy will point to old posts about this here, here, here, and here), but for now let’s just think about the ability to make predictions. I’m thinking specifically about predicting the future state of some biological system based on knowledge of its current state and the processes that cause it to change.

Despite its lovely theoretical apparatus, I’m not sure evolutionary biology is any better than ecology at making good predictions. (Actually, I have no idea, which is one reason to write a blog post and hope for illuminating input from you all.) Here’s a hypothetical example: if we know that someone is about to start dumping nutrients into a lake or that fires will become more frequent in some terrestrial ecosystem, how well can we predict the future states of ecological variables (e.g., community composition, total biomass) vs. evolutionary variables (e.g., intraspecific distributions of heritable traits, allele frequencies)? It seems to me that we can actually make half-decent ecological predictions, even if this is sometimes based only on predicting that the same thing will happen as the last time someone dumped nutrients in a lake (does that count?).

However, for any individual species, we might be highly uncertain even about whether it will persist (and therefore have the chance to adapt – although I guess that’s also a failed ecological prediction), and if it does persist, whether the direct selection pressure (nutrients or fire) will be countered or magnified by indirect effects (e.g., changing abundances of other species) and whether the adaptive significance of particular genes or traits is altered in the novel conditions, thus making predictions bunk or at least valid only over exceedingly short time scales. No doubt we could also list predictive successes of evolutionary biology (e.g., HIV treatment; please offer more in the comments), but hopefully I’ve made my point clearly enough: it’s not clear to me that we’re all that good at predicting future evolutionary trajectories of populations.

At the end of the day, does Team Evolution actually come out ahead of Team Ecology? What are the shining examples of success in each discipline? Should evolutionary biologists actually have ecology envy rather than vice versa? I’d love to know what you think.

*the book largely concerns communities of species on the same trophic level or at least those potentially competing for the same things (e.g., space), but this is not the place to get into all that.

** I use the word “read” in its broad sense, including (very light) skimming.

*** If it’s not obvious yet, I’m doing some tongue-in-cheek role-playing here – communicating extreme stances on things just to be provocative. I may or may not believe any of it.

46 thoughts on “Why do (some) ecologists have evolution envy?”

Interesting question, Mark, but two things suggest to me that it’s a bit of a straw man, or at least doesn’t really capture the way (some) ecologists think. The first thing is that “evolutionary biology” is a less cohesive field than the Darwin-Mendel-Modern Synthesis narrative implies, because it’s difficult if not impossible to apply models and theory about population genetics at a macro-evolutionary level to explain patterns in the fossil record. Sure, we can do some hand waving about micro-evolution scaling up to macro-evolution, but that’s about it: there is no grand theory to explain what we see in the fossil record.

The second thing that strikes me is that “ecology” and “evolutionary biology” merge seamlessly into one another to give us “evolutionary ecology”, which has been a dominant approach in many sub-fields of ecology, for example in my own area of pollination ecology via studies of phenotypic selection on floral traits, to give one example. Thus quite a number of “ecologists” would see themselves as just as much “evolutionary biologists”, and would publish in journals that span both disciplines.

Absolutely (I straddle the eco-evo divide myself, and indeed advocate such studies!). But…while we can do evolutionary ecology on one or a few focal species, how do we go about understanding the diversity and structure of a tropical forest with 500 species of tree in a small area? For the most part we build theories and collect data within which each organism is binned into a species category, within which we can’t possibly study evolutionary change simultaneously in all 500 species. In other words, there are big chunks of evolution-free ecology, which is entirely appropriate. My own point of departure is community ecology, and the source of envy is microevolution/population genetics. So, what looks like a straw man at a macro level (no pun intended) – that is “ecology vs. evolution” – might not be so easily dismissed in this more targeted context. (My pitch for the post may have been to broad.)

“while we can do evolutionary ecology on one or a few focal species, how do we go about understanding the diversity and structure of a tropical forest with 500 species of tree in a small area?”

But I wouldn’t see the diversity and structure question as an evolutionary topic per se: it’s a question about assembly of ecological communities, which typically occur over timescales of decades to 1000s of years, rather than on the (typical) timescales of speciation.

Can’t quite figure out how best to respond to Jeff’s comment about diversity and structure in this forest as not an evolutionary topic (no reply icon below it), so I’ve stuck it here…
Yes, I agree, and I suppose that’s my point: there are plenty of ecological questions without an evolutionary component. So, even if the two disciplines blend into one another (as you say), if you work somewhere away from the eco-evo ecotone (new jargon!), it’s a valid question to ask whether you feel envy (or not) when peering across.

I recall quite vividly in my first year of graduate school standing in front of two doors, one labelled “ecology” and one labelled “evolution” (I had been out of school for 9 years and knew this was the general field but was not particularly clear on exactly what and had intentionally chosen a school and adviser that would let me go either way after I got immersed in both fields).

At the end of that year I was decisively in the ecology camp, so no evolution envy here! It was exactly as Jeff has already said. There is this nice clear theory (which I taught myself out of Roughgarden’s book). But just like physics, it only worked in an imaginary land. In particular when what you were interested in was one or a few loci with one or a few allelles. And the whopping assumption that selection strength was a constant (OK a few mutterings allowing for fluctuating selection or density-dependent selection but those were hard and remain frontiers today – but the elegant theory was all “assume s=0.1”). At the same time I was taking a class on speciation and a class on biodiversity (with about half of it focused on evolution). It only took me about half a semester to realize that this nice, clear theory had nothing to say about the questions of interest like speciation (OK again we know divergence and isolation relate to speciation and can be modelled – but put the two together to predict rates of speciation or such, no chance). And then I discovered Lande’s phenotypic evolution and quantitative genetics. But they all require parameterizing this giant covariance matrix which realistically can be done only posthoc and in constant environments – exactly the same challenge as generalized lotka volterra models (so a very direct link to theory that is no better and no worse than ecology). So I walked through the ecology door. At least they were not fooling themselves about the complexity. Which is not to say I don’t have plenty of respect for my evolution colleagues who are tangling with just as much complexity as we ecologists. But my own particular path to evolution led me to a moment of disillusionment with that “elegant clean theory” that pushed me to ecology. In short, I felt like there was an oversell in evolution that I didn’t see in ecology. I’m sure others would see things exactly the opposite.

I think it is an interesting question why physics has been so successful with “physicsland” (“assume there is no air resistance”), while evolution has to my mind but much less successful with “evolutionland” (“assume your trait of interest is controlled by a single locus with two alleles and a constant fitness difference”). I think it is degrees of complexity. Physics only has 2 or 3 things going on simultaneously (air resistance, gravity, relativity) and many (but not all) real world scenarios can meaningfully ignore all but one (a falling cannonball doesn’t have much to do with air resistance except near terminal velocity and nothing to do with relativistic speeds). But I think evolution is more like ecology. Ten different things are going on, and the only place you can safely ignore 9 of them is in a mesocosm or very controlled experiment.

Just my two cents and my personal path. Its a great question though. And I’ll be very curious to see what others have to say!

Fascinating that the very same thing that draws some people to evolutionary biology repels others! I suppose there’s a universal truth there: for any one of multiple options (in whatever context), one person’s upside is another’s downside (I think there are idiom’s for that).

We might consider this as one answer to the question “Should evolutionary biologists actually have ecology envy?”. The answer here would appear to be “yes”, if the aim is to grapple with and understand the full complexity of nature. The score is 1-1 (I’m a hopeless sports junkie).

Brian, I’d like to challenge your notion that the clear nice theory of physics only works in an imaginary land. Take the practical success of thermodynamics, mechanics, nuclear physics, semiconductors, optics, physical chemistry, or even quantum mechanics — these disciplines are anything but imaginary land. And the advances in each of these disciplines would be impossible without the advances in the others, exactly because it is all linked together; Mark may call that the aesthetically pleasing quality.

Of course there are wild things like string theory, which seem to be difficult to use or empirically prove, but I wouldn’t generalize that to the whole physics.

I totally take your point, Petr. Physics is wildly successful in a way ecology has not been (and probably never will be).

And I was careful to say analyses in physics-land often did map to the real world. But there are also places where they don’t. Ask a physicist to make a prediction of when a feather dropped from 10 feet will hit the ground and they’ll give you a funny look. I spent 6 summers in high school and college working in an engineering shop writing computer code to simulate heating and cooling of buildings. It was an odd mix of incorporating basic physics principals and wild approximation models where the physics couldn’t handle the complexity of a diverse set of building materials, with living humans moving around, etc. The attitude of the engineers I worked with (and I think engineers in general) is that engineers live in the real world and physicists live in an imaginary place. I take that with a grain of salt though.

So to be clear. Every field has an imaginary land where there science applies best and the answers transfer but degrade in the real world. I think physics has a fairly high degree of transferability between physics-land and the real-world. But it doesn’t have perfect transferability. Ecology has a fairly low degree of transferability between ecology-land (Lotka Volterra only two species that move randomly and are well-mixed with constant environment and constant interaction strength) and the real-world. The non-transferability is not a short-coming or anybody’s fault. Its just important to recognize. And it does vary by field.

Oooh – and I forgot to say I was lucky enough to be an early reader of your book and its a game changer. Seriously everybody who wants to be part of the big discussions is going to have to read Mark’s book. Starting with your original paper and then the book, the questions you raise and approaches you suggest have been working their way through my head for years and profoundly influenced the way I think. I don’t agree with everything and nor will anybody else (and I know you don’t expect that), but just like Hubbell’s Neutral Theory, this book will be something people need to react to one way or the other!

I just finished reading this review of eco-evolutionary dynamics:http://onlinelibrary.wiley.com/doi/10.1111/nyas.12974/abstract
One of their main points is that our desire to have simple explanations for things (which I guess is the evolutionary view or evolution envy, as framed in this post) might be causing us to miss important drivers of dynamics in the real world. In other words, the simplest explanation might not be the correct one.

True enough. Although even for a single agreed-upon explanation for something, one can emphasize the details (e.g., plant A differs from plant B in terms of requirements for different nutrients, and susceptibility to pathogens 1, 2 and 3, as well as response to fires of different severity, the sum total of which allows coexistence) or on the key outcome (each species has a positive growth rate when rare). To a first approximation, ecologists often do the former, evolutionary biologists the latter.

And, all else equal, a simple explanation still seems more desirable than a complicated one!

The point about predictions is an interesting one, and possibly reaches the crux of how ecology and evolution are actually differentiated.

For instance, the Stuart et al study (Rapid evolution of a native species following invasion by a congener) where they looked at evolutionary responses of Anolis carolinensis in response to an invasion by another species of Anolis (A. sagrei). They predicted how A. carolinensis would adapt, and it did!, but they made that prediction based on ecological interactions. So which “team” gets credit for that? In general, I think a lot of the examples you list, like predicting patterns in the fossil record, really can’t be broken down into one team or another because predicting, for instance, that ornithurine birds would radiate while enantiornithine birds would go extinct at the KPg is most definitely an eco-evolutionary problem.

Really, if we want to compare “team evolution” with “team ecology”, we need to look for questions where the two aren’t conflated. That is, we need -pure- evolutionary questions, as in questions that can be addressed using only evolutionary theory. A few that spring to my mind are:

1) Dobzhansky and Muller both predicted that reproductive incompatabilities would accumulate between species through time non-linearly (faster than linearly, specifically) and work by Daniel Matute bore that out (A Test of the Snowball Theory for the Rate of Evolution of Hybrid Incompatibilities).

2) Haldane’s rule about incompatibilities accumulating preferentially in sexes that have two distinct sex chromosomes has been born out time and time again (birds, mammals, leopard geckos, butterflies? I think…).

3+) Evolutionary game theory as it relates to sneaker male: dominant male ratios in wild populations & sneaker male : dominant male sperm quality. Or really a lot of other things. Evolutionary game theory, in its various forms, has made lots of predictions and a couple other things could (arguably) be lumped into the overarching framework. Kin selection, for instance, has some game theoretic properties.

Indeed ecology and evolution are often intertwined. I can’t comment on your specific examples, but one thought seems relevant here: you can’t really do evolutionary biology without ecology, but you can do ecology without evolutionary biology. Somewhat tongue-in-cheek, I often note in seminars that the evolutionary biologist’s definition of ecology is anything that happens out of doors. That is, unless your organism is trapped in a tube and you exert the evolutionary forces yourself, ecology will be part of your study. In contrast, one can study community structure or nutrient cycling without any direct reference to evolution.

I’m not sure I buy the ecology-without-evolution-is-possible argument. At least, I don’t buy it broadly.

Basically, in my view, ecology without direct reference to evolution only works on extremely restrictive spatial and temporal scales. Certainly you can document the structure of a community or how nutrients cycle through a community in a particular locality for a year or so without needing to appeal to evolution. So I agree with your point to an extent. But my somewhat tongue-in-cheek retort would be that once you move beyond a small spatial scale, or to a timeframe that’s longer than a couple of years (multiple generations for lots of ecologically important species!) direct reference to evolution becomes necessary.

On the other hand, evolutionary-prediction-without-ecology certainly happens, even in the dreaded land that exists out-of-doors. Haldane’s rule applies to any (heterogametic) organism, anywhere on Earth, in any environment, at any trophic level (the only way to have an exception is weird genomic/maternal effects that are divorced from ecology). Game theory predictions about ratios of different strategies don’t take trophic level, habitat, predators, prey or any other ecological factor into account, either, and they work pretty well at predicting actual patterns (ratios of strategies) in natural settings.

Great post Mark. I count myself as a member of Team Evolution, but you made me stop and think. That’s rare, and I love it when it happens.*

A few thoughts, some of which will echo some things Brian and others have said above:

-I think the ideal field is one in which you have both a unifying general framework of the sort that evolution has, *and* you have successful models that aren’t general–they purchase their success by being system- or case-specific in various ways. The unifying framework keeps your system-specific models from just being an unrelated collection of unique special cases having nothing to do with one another. That’s tremendously valuable, even if the unifying framework itself doesn’t make any predictions or provide any explanations, and even if we don’t have many case-specific models that make successful predictions or provide successful explanations either. For instance, I think the best contemporary population ecology has both a unifying framework built on a few key unifying concepts (delta N = B -D + I – E; density-dependence vs. independence, nonlinearities, time lags, envi. and demogr. stochas., population structure…that’s mostly it), together with successful statistical and mechanistic models of various special cases. And it’s tremendously successful (https://dynamicecology.wordpress.com/2012/01/30/statisticians-meet-ecologists/; https://dynamicecology.wordpress.com/2012/12/12/ecological-success-stories/)

-I think we tend to take for granted that evolutionary biology has the unifying framework it does, and we only realize it when we compare it with a field that lacks one, like community ecology. I mean, try to imagine what evolutionary biology would look like if no one had thought of the concepts of “selection”, “mutation”, “migration”, and “drift”. It would look like…community ecology! (at least, community ecology before Mark came along!) Evolutionary biologists may not be able to predict the behavior of specific systems any better than community ecologists. But at least they don’t ever complain that their field is just a senseless mess of unique case studies and argue that everyone give up on it for that reason! And it’s not as if evolutionary biology is intrinsically any simpler than community ecology. I have an old post on this: https://dynamicecology.wordpress.com/2011/04/21/synthesizing-ecology-revisiting-an-oikos-classic/

-I think there are other things a unifying framework gives you, besides the satisfying-to-many-but-ultimately-subjective feeling that the field is “unified”. For instance, if you don’t have a unifying concept like selection, you can’t go out and measure the strength of selection in lots of different systems and then do a meta-analysis on the results to look for patterns which might test theoretical predictions or inspire new theory (e.g., Kingsolver et al. 2001 meta-analysis of field estimates of selection). If you don’t have a unifying concept like “density dependence” in population ecology, you can’t go out and estimate density dependence in lots of different systems. Etc. I would think that this argument in particular should resonate with the NCEAS/SESYNC/NESCent/et al. generation (I’m looking at you, Brian!)

-Another thing you gain from a unified theoretical framework like evolution has is the ability to recognize possibilities you wouldn’t otherwise have thought of. If you’re trying to build up a general understanding of the world from the bottom up, by comparing and contrasting lots of different system-specific models in the absence of a unifying framework into which they all fit, you’re liable to overlook some of the possibilities. I have an old post arguing that this is exactly what happened to metacommunity ecology, leading to widespread, serious mistakes. Leibold et al. 2004 sketched out a framework for metacommunities by organizing the various case-specific metacommunity models that had been proposed at the time. They identify four “types” of metacommunities. They of course recognize that those four types are all limiting cases and say that real metacommunities will be somehow intermediate between those limiting cases. But if you look at the analogous body of theory in evolutionary biology, you discover multiple *other* limiting cases that ecologists have totally overlooked. Evolutionary biologists didn’t identify those other cases because they’re more imaginative than community ecologists. They recognized them because they think in terms of that unifying theoretical framework and its associated short list of key processes–selection, mutation, migration, drift. It is *much* easier to fully explore the range of possibilities implied by a four-dimensional space than by a gazillion-dimensional space. So Leibold et al. 2004 is actually a seriously incomplete and misleading roadmap to the full range of possible metacommunity dynamics. I have an old post expanding on this: https://dynamicecology.wordpress.com/2013/10/17/what-metacommunity-ecology-can-learn-from-population-genetics/

-I do think evolutionary biologists in some subfields need more in the way of case-specific, more biologically rich models that don’t just treat selection, migration, mutation, and drift as parameters. You need models allowing those “high level” parameters like selection and drift to emerge from assumptions about other, “lower level” features of the system. The trouble with trying to build your case-specific models by making explicit assumptions about selection, migration, mutation, and population size (the latter of which implies the strength of drift) is that you tend to forget that those parameters often are not independent of one another. And it can feel very artificial and ad hoc to assume some correlation between them, or to assume that one or more of those parameters varies over space or time in some complicated way It feels like you’re “rigging” the model (e.g., assuming that selection is frequency-dependent in the just the right way so as to allow genetic diversity to be stably maintained). When of course, in nature it may well be that, in system X, the biology of the system really does cause those parameters to vary in some complicated, intercorrelated way. I thought I had an old post on this, but I can’t find it now. I think this has misled evolutionary biologists to thinking that it’s really hard for temporal environmental variation to maintain diversity of competing alleles. When in fact, the conditions required for this to work only look sort of weird or restrictive or difficult to achieve if you specify your model in terms of selection coefficients and population sizes. If you write down the underlying biology and then let selection, drift, etc. emerge from that, it’s kind of hard *not* to write down a model that doesn’t allow some possibility for temporal environmental variation to promote coexistence.

One other thought unrelated to the main thrust of the post: one side effect of having a unifying framework for one’s field is that it tells you what’s *not* part of that field. If “community ecology” is defined as Mark suggests, one effect is to kick out large chunks of the field as being just a different subject. Lots of food web ecology, for starters, except insofar as trophic interactions affect coexistence within trophic levels. Ecosystem function is out too. Just as macroevolution isn’t really part of microevolution (though macroevolution has its own unifying framework. Species are “born” via speciation, and they “die” via extinction. They have “fitness” in the form of the number of descendant species they leave at some later date. Etc. So you can talk about species selection, which is perfectly analogous to its microevolutionary counterpart. As illustrated by the fact that the Price equation applies equally well to both.)

Man, I wish we had this exchange before I finished writing the book! Not surprisingly, I agree with the points about the value of a general framework “even if the unifying framework itself doesn’t make any predictions or provide any explanations”. Your comments echo some of my own in the book, and I definitely could have usefully borrowed some examples and phrasings. I haven’t fully thought through what implications evolutionary biology might draw from looking at how community ecology is done – good points here as well about that. Fully developing the latter seems like a good sabbatical project for a “real” evolutionary biologist…

SQUEE! Mark Vellend is doing guest posts here! Vellend 2010 is, bar none, my favorite paper ever, and I am very much looking forward to its follow ups — book, posts, whatever. (Only gripe: “late summer” is a Long. Time. Away.) I came from computer science — a highly organized discipline — and spent years trying to wrap my head around and mentally organize all the ecology I was learning as a PhD student. To little avail, until I read Vellend 2010. After 6+ years in community ecology, I’m doing more ecosystems and macroecology now, and it feels a lot better organized. But I was just now wading back into community ecology to finish up publication of some dissertation chapters, and bemoaning its messiness.

I have definitely experienced evolution envy on occasion. While it’s a good point that evolution is also messy and not necessarily predictive, I think the envy comes from the type of messiness. I feel like in evolution, the messiness stems from complex biological networks that nonetheless have an atomic base (e.g. a gene or an individual), just as computer networks can be very complex, but basically boil down to servers connected to one another. In community ecology, I’m not even sure we agree about what level things ought to be studied at — species? individuals? communities? assemblages? I feel like the questions are less well defined than in evolution. And I definitely feel like evolution has higher math expectations, so that you can do more sophisticated statistics and modeling and not lose everyone in the process. This could all be grass-is-greener envy, though, as I haven’t done evolution research myself.

I’m wondering, though, if eventually a full framework for community ecology is going to have to include evolution. If you’re going to consider immigration and emigration (of terrestrial plants, say), you’re automatically working at generational timescales, and so under any sort of dynamics or change scenario, evolution is a definite potential player.

At the risk of causing you to implode with excitement, you can look forward to further guest posts from Mark over the next few months, to tide you over until his book comes out. 🙂

“This could all be grass-is-greener envy, though, as I haven’t done evolution research myself.”

I’ve been wondering about that myself. I would be *very* interested to hear from some proper evolutionary biologists on this thread.

Also, very interesting remark about computer networking. I’m always curious to hear how people in other fields see their own field, and I’d never heard from anyone in a position to know how computer scientists see computer science. I know lots of sociologists and anthropologists see their own fields as disunified, ill-defined messes, if anything to a greater extent than community ecology.

If I’m honest, that’s part of what I’ve always liked about ecology, actually, community ecology in particular. The field isn’t so well-defined that everyone agrees on exactly what to do next and so they all go out and do it. So there’s a need for conceptual and philosophical argument about what the field is and how to go about doing it (Contrast particle physics: we all agree that we should look for the Higgs boson, so we all agree that we should build a whopping great particle smasher and go work there. At one level that’s great–it’s a recipe for steady progress–but it’s also a bit boring, intellectually.) But ecology as a field isn’t *so* ill-defined that there’s no possibility of the field ever defining itself or making progress that everyone in the field would recognize as progress. (Contrast, say, large chunks of anthropology and sociology).

“If I’m honest, that’s part of what I’ve always liked about ecology, actually, community ecology in particular.”

Me, too. Computer science was a little bit *too* organized for my liking. It seemed too easy. (Where what I mean by “easy” is: it’s clear that we can figure out X if we spend long enough working on the problem. Like: we can build a database system that provably runs the fastest given a particular hardware architecture and particular use application.) I’ve always liked problems where you don’t know if you’ll actually come up with an answer, and I was was drawn to CS problems that interface with real-world messiness, such as computer vision, natural language comprehension, etc.

Oh my. If I was more web savvy, I’d stick a little blushing face here. Thanks for your very generous comments. To respond to some specific points you raise:

– I think the atomic base of community ecology can be an individual organism. Predictions can be generated at the level of plots (# species) or populations (increasing from rare) or species (colonization-competition tradeoffs), but they can all flow from models of individual interacting organisms. Same is true in evolutionary biology (base pairs, genes, phenotypes, individuals, populations).

– I find interesting the question of whether community ecology ultimately _needs_ evolution for a “full framework”. Along with many others, I have made arguments that evolution is definitely needed to understand what’s happening in some particular cases. That said, each branch of science bleeds into others, and so a full framework for anything will ultimately involve everything if you take the argument far enough. A plant community ecologist draws frequently on geology, soil science, physiology, and biogeochemistry (and evolution) to make sense of particular outcomes of interest, but it doesn’t mean that you can’t have a framework for community ecology without _formal_ incorporation of those other fields of science. The composition and diversity of species in a given place are dependent variables of interest, specific to community ecology, and I think we can define underlying community-level processes that themselves are underlain by a gazillion things. Rapid evolution is one of the latter, but I’m not sure it’s one whose importance needs to be elevated above the others.

very interesting post and great discussion. thx a lot.
I have a terminological side question to this, sorry for being nit-picky here: if you use community in the narrow sense that Mark described here, how do you actually call all the organisms living together in one place?

as a food web ecologist I always considered all living things in a given locality as community and this definition is backed up by several classic ecology textbooks while the “community sensu Vellend” is rather defined as a guild (also see Stroud et al 2015 Ecology & Evolution “Is a community still a community? Reviewing definitions of key terms in community ecology” doi: 10.1002/ece3.1651)

@Jeremy: well the paper I cited does explicitly revisit the paper by Fauth et al and confronts it with current survey data from ecologists of all career stages. not surprisingly (as you say), their conclusions are not very optimistic. what I found quite remarkable is that it this paper was written by a team composed exclusively of graduate students, so junior people who were trying to figure out things (relatively) new to them. (i dont know if the key motivation for this paper was their “frustration” about this terminological mess or rather their curiosity)

relating this in a “big picture” approach to the core topic of the post I think we might face here a sort of a chicken and egg problem: if your terminology is a mess, how can you expect to come up with a clean and sound core conceptual/theoretical body and vice versa?

Oh, I agree that terminological messiness is a symptom of underlying conceptual issues in a field. But I disagree that terminological messiness is a significant cause of conceptual confusion, though I’m sure it makes life difficult for beginning grad students. Because I think terminological messiness is a symptom, I think attempts to fix it like Fauth et al.’s and Stroud et al.’s are doomed to failure. You can’t cure the disease by treating the symptoms. And this is one of those cases where I doubt the symptoms can be controlled except by treating the disease.

I have struggled with terminology, so your point is well taken. As you say, community ecology is a broader umbrella than implied by how I’ve been using the term. Here’s how I deal with things in my book:

– Community sensu lato = “all living things in a given locality” (as you say)

The problem is that at least 99% of empirical and theoretical studies in community ecology focus on a subset of this ideal, so we can’t reasonably insist on this as the one and only definition. Common community subsets include:

– Food web (things that eat one another, with “things” often not resolved to the species level, competition often not in the models)
– Mutualistic network (two groups of interacting species – plants and their pollinators is a common example)
– Community module (small number of strongly interacting species)
– Horizontal community (sort of = guild, sort of = assemblage, sort of = trophic level)

Regarding the latter category, the terms are “all decidedly lacking in the pizazz and the admirable self-defining quality of the other terms” (quote from book), so I just went with “community”. I hope we come up with a better term some day. I’d be happy to use it.

“The problem is that at least 99% of empirical and theoretical studies in community ecology focus on a subset….”

I’d go further and say it’s 100% because there are no studies (theoretical or otherwise) that I’m aware of that include the microbiota, which can be the dominant component of the community in terms of abundance, and certainly are in terms of richness. Our understanding of community ecology sensu lato is wholly skewed towards the larger component of the biota, which is understandable of course.

Mark: many thanks for your answers. you are right, the relationship between terminology and actual meaning is strongly affected by data availability (or rather lack thereof) because who is able to sample everything from smallest bacteria to largest verts in the same location at the same time? still I am optimistic that with further development and integration of, for instance, molecular methods the situation should improve in the long run.
anyhow I am pretty much looking forward to read the book.

@jeffollerton: I almost never say “never” or “always” or “100%” because there’s usually at least one exception that gets pointed out, even if I suspect it really is 100%. So, I most likely totally agree.

Some of them do. And some others are sufficiently careful that there’s no contamination. For instance, Kaunzinger & Morin 1998 Nature. Or any one of many bacteria- or algae-only experiments: Classic chemostat stuff from Tilman, Grover, the Kilhams, etc., experimental eco-evolutionary dynamics from folks like Andy Gonzalez, Graham Bell, Rees Kassen, Angus Buckling, and many others…

At the risk of being in the wrong pond, I thought I’d jump in from the computer science/evolutionary biology side. Like Margaret, my background is in CS, but then I jumped into evolutionary biology and combined things into artificial life, i.e. evolution in a computer. I love the structure of CS, but also found the problems to seem too obvious. However, I apparently didn’t want to jump so far into the messy to hit ecology and stopped nicely at evolution :). I absolutely love that every problem I’m interested in boils down to an atomic framework, but I would definitely say that the interesting problems get to a point where there are so many complex interactions that we struggle to predict what will happen. After all, if it was easy to predict, it’d be boring. A lot of what I do is use computer programs to try to figure out how parameter values and things lead to predictable dynamics that could let us reliably predict how evolution would play out in a particular system. But obviously if it was easy, I would be sad because I would be out of a job!

I find this discussion fascinating because I’ve always set out with the goal of finding that basic framework that would give biology the foundation that physics has, and I think that’s how I wound up trying to figure that out for complex evolutionary systems.

Interesting to hear that from your perspective, evolutionary biology is intermediate in it’s “structure” between ecology and CS.

I’m curious whether your view would be shared by, say, someone who’d come into evolutionary biology from a very different direction, or who was doing empirical work in evolution rather than modeling. Do you have any sense of that?

I mostly work with empirical biologists focused on unicellular systems, but there were plenty of students in my cohort that worked with macrofauna, which gets a whole lot messier very quickly. I guess I’m biased since at my center the whole goal is for all of us to find generalizable dynamics across every system we can study. I’ll certainly ask the next time I’m on campus!

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